library("here")
library("sjPlot")
library("tidyverse")
library("lme4")
library("viridis")
library("lmerTest")
library("ggplot2")
library("gridExtra")
library("gt")
library("ggthemes")
source(here("p4_analysis", "analysis_functions.R"))
p4path <- here("p4_analysis", "outputs")
p3path <- here("p3_methods", "outputs")
1. MMRR
1.1 Individual sampling
1.1.1 Summary plots
mmrr_ind <- format_mmrr(here(p3path, "mmrr_indsampling_results.csv"))
# overall error
summary_hplot(mmrr_ind, "ratio_ae", colpal = "viridis", direction = -1)

summary_hplot(mmrr_ind, "env_ae", colpal = "viridis", direction = -1)

summary_hplot(mmrr_ind, "geo_ae", colpal = "viridis", direction = -1)

# bias
summary_hplot(mmrr_ind, "ratio_err", divergent = TRUE)

summary_hplot(mmrr_ind, "comboenv_err", divergent = TRUE)

summary_hplot(mmrr_ind, "geo_err", divergent = TRUE)

1.1.2 Model summaries
run_lmer(mmrr_ind, "ratio_ae", filepath = here(p4path, "MMRR_individual_ratioerr.csv"))
[[1]]
|
Predictors
|
Fixed Effects
|
Sum Sq
|
Mean Sq
|
NumDF
|
DenDF
|
F value
|
Pr(>F)
|
|
nsamp
|
−0.0003
|
8.0000
|
8.0000
|
1
|
15.3480K
|
1531.810409
|
1.9 ×
10−319
|
|
sampstrat
|
0.1078
|
1.5728
|
0.5243
|
3
|
15.3480K
|
100.385844
|
2.4 ×
10−64
|
|
K
|
0.0126
|
0.6090
|
0.6090
|
1
|
15.3480K
|
116.601181
|
4.4 ×
10−27
|
|
m
|
0.0523
|
10.5133
|
10.5133
|
1
|
15.3480K
|
2013.030919
|
0.0
|
|
phi
|
−0.0020
|
0.0155
|
0.0155
|
1
|
15.3480K
|
2.971532
|
0.085
|
|
H
|
0.0096
|
0.3544
|
0.3544
|
1
|
15.3480K
|
67.857541
|
1.9 ×
10−16
|
|
r
|
−0.0090
|
0.3123
|
0.3123
|
1
|
15.3480K
|
59.800103
|
1.1 ×
10−14
|
[[2]]
|
Contrast
|
Estimate
|
SE
|
Z ratio
|
p
|
|
EG - G
|
−0.0046
|
0.0016
|
−2.7960
|
0.02658997
|
|
EG - R
|
−0.0009
|
0.0016
|
−0.5319
|
0.95132229
|
|
EG - T
|
−0.0249
|
0.0016
|
−15.0697
|
0.0
|
|
G - R
|
0.0037
|
0.0016
|
2.2641
|
0.10653142
|
|
G - T
|
−0.0202
|
0.0016
|
−12.2737
|
0.0
|
|
R - T
|
−0.0240
|
0.0016
|
−14.5378
|
0.0
|
run_lmer(mmrr_ind, "geo_ae", filepath = here(p4path, "MMRR_individual_geoerr.csv"))
[[1]]
|
Predictors
|
Fixed Effects
|
Sum Sq
|
Mean Sq
|
NumDF
|
DenDF
|
F value
|
Pr(>F)
|
|
nsamp
|
−0.0001
|
1.2007
|
1.2007
|
1
|
15.3480K
|
1.422367e+03
|
9.2 ×
10−298
|
|
sampstrat
|
0.0525
|
1.3073
|
0.4358
|
3
|
15.3480K
|
5.161977e+02
|
1.3 ×
10−319
|
|
K
|
0.0060
|
0.1396
|
0.1396
|
1
|
15.3480K
|
1.653996e+02
|
1.2 ×
10−37
|
|
m
|
0.0215
|
1.7801
|
1.7801
|
1
|
15.3480K
|
2.108706e+03
|
0.0
|
|
phi
|
−0.0007
|
0.0018
|
0.0018
|
1
|
15.3480K
|
2.107968e+00
|
0.15
|
|
H
|
0.0056
|
0.1183
|
0.1183
|
1
|
15.3480K
|
1.401947e+02
|
3.3 ×
10−32
|
|
r
|
0.0000
|
0.0000
|
0.0000
|
1
|
15.3480K
|
5.586296e-03
|
0.94
|
[[2]]
|
Contrast
|
Estimate
|
SE
|
Z ratio
|
p
|
|
EG - G
|
−0.0021
|
0.0007
|
−3.1344
|
0.009333831
|
|
EG - R
|
−0.0010
|
0.0007
|
−1.5145
|
0.428730954
|
|
EG - T
|
−0.0223
|
0.0007
|
−33.5784
|
0.0
|
|
G - R
|
0.0011
|
0.0007
|
1.6199
|
0.367294910
|
|
G - T
|
−0.0202
|
0.0007
|
−30.4440
|
0.0
|
|
R - T
|
−0.0213
|
0.0007
|
−32.0639
|
0.0
|
run_lmer(mmrr_ind, "env_ae", filepath = here(p4path, "MMRR_individual_enverr.csv"))
[[1]]
|
Predictors
|
Fixed Effects
|
Sum Sq
|
Mean Sq
|
NumDF
|
DenDF
|
F value
|
Pr(>F)
|
|
nsamp
|
−0.0001
|
0.7452
|
0.7452
|
1
|
15.3480K
|
1940.537011
|
0.0
|
|
sampstrat
|
0.0350
|
0.0609
|
0.0203
|
3
|
15.3480K
|
52.882904
|
5.4 ×
10−34
|
|
K
|
0.0017
|
0.0105
|
0.0105
|
1
|
15.3480K
|
27.383651
|
1.7 ×
10−7
|
|
m
|
0.0095
|
0.3480
|
0.3480
|
1
|
15.3480K
|
906.190605
|
1.8 ×
10−193
|
|
phi
|
−0.0003
|
0.0004
|
0.0004
|
1
|
15.3480K
|
1.026801
|
0.31
|
|
H
|
0.0014
|
0.0078
|
0.0078
|
1
|
15.3480K
|
20.317704
|
6.6 ×
10−6
|
|
r
|
−0.0026
|
0.0262
|
0.0262
|
1
|
15.3480K
|
68.233041
|
1.6 ×
10−16
|
[[2]]
|
Contrast
|
Estimate
|
SE
|
Z ratio
|
p
|
|
EG - G
|
−0.0005
|
0.0004
|
−1.2011
|
0.6261246
|
|
EG - R
|
0.0005
|
0.0004
|
1.0594
|
0.7142981
|
|
EG - T
|
−0.0045
|
0.0004
|
−10.1643
|
3.7 ×
10−14
|
|
G - R
|
0.0010
|
0.0004
|
2.2605
|
0.1074219
|
|
G - T
|
−0.0040
|
0.0004
|
−8.9632
|
2.7 ×
10−14
|
|
R - T
|
−0.0050
|
0.0004
|
−11.2237
|
8.0 ×
10−15
|
1.1.3 Megaplots
MEGAPLOT(mmrr_ind, "ratio_ae", colpal = "viridis", direction = -1)

MEGAPLOT(mmrr_ind, "ratio_err", colpal = "viridis", divergent = TRUE)

MEGAPLOT(mmrr_ind, "comboenv_err", divergent = TRUE)

MEGAPLOT(mmrr_ind, "geo_err", divergent = TRUE)

1.2 Site sampling
1.2.1 Summary plots
mmrr_site <- format_mmrr(here(p3path, "mmrr_sitesampling_results.csv"))
# overall error
summary_hplot(mmrr_site, "ratio_ae", colpal = "viridis", direction = -1)

summary_hplot(mmrr_site, "env_ae", colpal = "viridis", direction = -1)

summary_hplot(mmrr_site, "geo_ae", colpal = "viridis", direction = -1)

# bias
summary_hplot(mmrr_site, "ratio_err", divergent = TRUE)

summary_hplot(mmrr_site, "comboenv_err", divergent = TRUE)

summary_hplot(mmrr_site, "geo_err", divergent = TRUE)

1.2.2 Model summaries
run_lmer(mmrr_site, "ratio_ae", filepath = here(p4path, "MMRR_site_ratioerr.csv"))
[[1]]
|
Predictors
|
Fixed Effects
|
Sum Sq
|
Mean Sq
|
NumDF
|
DenDF
|
F value
|
Pr(>F)
|
|
nsamp
|
−0.0054
|
10.9296
|
10.9296
|
1
|
8.6290K
|
774.65997483
|
2.4 ×
10−163
|
|
sampstrat
|
0.2519
|
1.7532
|
0.8766
|
2
|
8.6290K
|
62.13047517
|
1.6 ×
10−27
|
|
K
|
0.0003
|
0.0002
|
0.0002
|
1
|
8.6290K
|
0.01177688
|
0.910
|
|
m
|
0.0196
|
0.8264
|
0.8264
|
1
|
8.6290K
|
58.57599989
|
2.2 ×
10−14
|
|
phi
|
−0.0053
|
0.0618
|
0.0618
|
1
|
8.6290K
|
4.37735932
|
0.036
|
|
H
|
0.0115
|
0.2873
|
0.2873
|
1
|
8.6290K
|
20.36485607
|
6.5 ×
10−6
|
|
r
|
−0.0184
|
0.7299
|
0.7299
|
1
|
8.6290K
|
51.73067018
|
6.9 ×
10−13
|
[[2]]
|
Contrast
|
Estimate
|
SE
|
Z ratio
|
p
|
|
EG - EQ
|
−0.0340
|
0.0031
|
−10.8514
|
2.7 ×
10−14
|
|
EG - R
|
−0.0101
|
0.0031
|
−3.2163
|
3.7 ×
10−3
|
|
EQ - R
|
0.0239
|
0.0031
|
7.6351
|
8.6 ×
10−14
|
run_lmer(mmrr_site, "geo_ae", filepath = here(p4path, "MMRR_site_geoerr.csv"))
[[1]]
|
Predictors
|
Fixed Effects
|
Sum Sq
|
Mean Sq
|
NumDF
|
DenDF
|
F value
|
Pr(>F)
|
|
nsamp
|
−0.0011
|
0.4225
|
0.4225
|
1
|
8.6290K
|
131.605173
|
3.0 ×
10−30
|
|
sampstrat
|
0.0702
|
0.1566
|
0.0783
|
2
|
8.6290K
|
24.386603
|
2.7 ×
10−11
|
|
K
|
0.0329
|
2.3423
|
2.3423
|
1
|
8.6290K
|
729.634765
|
2.5 ×
10−154
|
|
m
|
0.1628
|
57.2736
|
57.2736
|
1
|
8.6290K
|
17841.045336
|
0.0
|
|
phi
|
−0.0069
|
0.1030
|
0.1030
|
1
|
8.6290K
|
32.099252
|
1.5 ×
10−8
|
|
H
|
0.0093
|
0.1876
|
0.1876
|
1
|
8.6290K
|
58.425672
|
2.3 ×
10−14
|
|
r
|
0.0036
|
0.0275
|
0.0275
|
1
|
8.6290K
|
8.564055
|
3.4 ×
10−3
|
[[2]]
|
Contrast
|
Estimate
|
SE
|
Z ratio
|
p
|
|
EG - EQ
|
0.0098
|
0.0015
|
6.5604
|
1.6 ×
10−10
|
|
EG - R
|
0.0018
|
0.0015
|
1.2065
|
0.4492773
|
|
EQ - R
|
−0.0080
|
0.0015
|
−5.3539
|
2.6 ×
10−7
|
run_lmer(mmrr_site, "env_ae", filepath = here(p4path, "MMRR_site_enverr.csv"))
[[1]]
|
Predictors
|
Fixed Effects
|
Sum Sq
|
Mean Sq
|
NumDF
|
DenDF
|
F value
|
Pr(>F)
|
|
nsamp
|
−0.0021
|
1.5946
|
1.5946
|
1
|
8.6290K
|
907.15865165
|
1.4 ×
10−189
|
|
sampstrat
|
0.0968
|
0.2031
|
0.1016
|
2
|
8.6290K
|
57.77799916
|
1.2 ×
10−25
|
|
K
|
−0.0003
|
0.0002
|
0.0002
|
1
|
8.6290K
|
0.09364113
|
0.760
|
|
m
|
0.0056
|
0.0679
|
0.0679
|
1
|
8.6290K
|
38.64283865
|
5.3 ×
10−10
|
|
phi
|
−0.0018
|
0.0074
|
0.0074
|
1
|
8.6290K
|
4.18983195
|
0.041
|
|
H
|
0.0027
|
0.0153
|
0.0153
|
1
|
8.6290K
|
8.69527846
|
3.2 ×
10−3
|
|
r
|
−0.0066
|
0.0936
|
0.0936
|
1
|
8.6290K
|
53.23325588
|
3.2 ×
10−13
|
[[2]]
|
Contrast
|
Estimate
|
SE
|
Z ratio
|
p
|
|
EG - EQ
|
−0.0116
|
0.0011
|
−10.4807
|
2.7 ×
10−14
|
|
EG - R
|
−0.0035
|
0.0011
|
−3.1708
|
4.3 ×
10−3
|
|
EQ - R
|
0.0081
|
0.0011
|
7.3099
|
8.2 ×
10−13
|
1.2.3 Megaplots
MEGAPLOT(mmrr_site, "ratio_ae", colpal = "viridis", direction = -1)

MEGAPLOT(mmrr_site, "ratio_err", divergent = TRUE)

MEGAPLOT(mmrr_site, "comboenv_err", divergent = TRUE)

MEGAPLOT(mmrr_site, "geo_err", divergent = TRUE)

2. GDM
2.1 Individual sampling
2.1.1 Summary plots
gdm_ind <- format_gdm(here(p3path, "gdm_indsampling_results.csv"))
# overall error
summary_hplot(gdm_ind, "ratio_ae", colpal = "viridis", direction = -1)

summary_hplot(gdm_ind, "env_ae", colpal = "viridis", direction = -1)

summary_hplot(gdm_ind, "geo_ae", colpal = "viridis", direction = -1)

# bias
summary_hplot(gdm_ind, "ratio_err", divergent = TRUE)

summary_hplot(gdm_ind, "comboenv_err", divergent = TRUE)

summary_hplot(gdm_ind, "geo_err", divergent = TRUE)

2.1.2 Model summaries
run_lmer(gdm_ind, "ratio_ae", filepath = here(p4path, "GDM_individual_ratioerr.csv"))
[[1]]
|
Predictors
|
Fixed Effects
|
Sum Sq
|
Mean Sq
|
NumDF
|
DenDF
|
F value
|
Pr(>F)
|
|
nsamp
|
−0.0003
|
9.4335
|
9.4335
|
1
|
15.3430K
|
8.826316e+02
|
1.2 ×
10−188
|
|
sampstrat
|
0.1099
|
0.7896
|
0.2632
|
3
|
15.3430K
|
2.462613e+01
|
6.9 ×
10−16
|
|
K
|
0.0231
|
2.0533
|
2.0533
|
1
|
15.3430K
|
1.921097e+02
|
2.0 ×
10−43
|
|
m
|
0.0799
|
24.4973
|
24.4973
|
1
|
15.3430K
|
2.292047e+03
|
0.0
|
|
phi
|
−0.0003
|
0.0002
|
0.0002
|
1
|
15.3430K
|
2.244788e-02
|
0.88
|
|
H
|
0.0020
|
0.0153
|
0.0153
|
1
|
15.3430K
|
1.435012e+00
|
0.23
|
|
r
|
−0.0057
|
0.1237
|
0.1237
|
1
|
15.3430K
|
1.157134e+01
|
6.7 ×
10−4
|
[[2]]
|
Contrast
|
Estimate
|
SE
|
Z ratio
|
p
|
|
EG - G
|
−0.0135
|
0.0024
|
−5.7327
|
5.9 ×
10−8
|
|
EG - R
|
−0.0097
|
0.0024
|
−4.0895
|
2.5 ×
10−4
|
|
EG - T
|
−0.0198
|
0.0024
|
−8.3745
|
4.1 ×
10−14
|
|
G - R
|
0.0039
|
0.0024
|
1.6429
|
0.35443857
|
|
G - T
|
−0.0062
|
0.0024
|
−2.6417
|
0.04111055
|
|
R - T
|
−0.0101
|
0.0024
|
−4.2845
|
1.1 ×
10−4
|
run_lmer(gdm_ind, "geo_ae", filepath = here(p4path, "GDM_individual_geoerr.csv"))
[[1]]
|
Predictors
|
Fixed Effects
|
Sum Sq
|
Mean Sq
|
NumDF
|
DenDF
|
F value
|
Pr(>F)
|
|
nsamp
|
−0.0012
|
106.3637
|
106.3637
|
1
|
15.3430K
|
2702.1766883
|
0.0
|
|
sampstrat
|
0.6274
|
17.5095
|
5.8365
|
3
|
15.3430K
|
148.2767520
|
1.0 ×
10−94
|
|
K
|
0.0235
|
2.1250
|
2.1250
|
1
|
15.3430K
|
53.9860038
|
2.1 ×
10−13
|
|
m
|
−0.0852
|
27.8979
|
27.8979
|
1
|
15.3430K
|
708.7482021
|
1.1 ×
10−152
|
|
phi
|
0.0073
|
0.2059
|
0.2059
|
1
|
15.3430K
|
5.2302675
|
0.022
|
|
H
|
−0.0033
|
0.0413
|
0.0413
|
1
|
15.3430K
|
1.0486234
|
0.310
|
|
r
|
0.0020
|
0.0148
|
0.0148
|
1
|
15.3430K
|
0.3751083
|
0.540
|
[[2]]
|
Contrast
|
Estimate
|
SE
|
Z ratio
|
p
|
|
EG - G
|
0.0899
|
0.0045
|
19.8550
|
0.0
|
|
EG - R
|
0.0204
|
0.0045
|
4.5115
|
3.8 ×
10−5
|
|
EG - T
|
0.0493
|
0.0045
|
10.8910
|
4.0 ×
10−14
|
|
G - R
|
−0.0695
|
0.0045
|
−15.3432
|
0.0
|
|
G - T
|
−0.0406
|
0.0045
|
−8.9659
|
2.7 ×
10−14
|
|
R - T
|
0.0289
|
0.0045
|
6.3789
|
1.1 ×
10−9
|
run_lmer(gdm_ind, "env_ae", filepath = here(p4path, "GDM_individual_enverr.csv"))
[[1]]
|
Predictors
|
Fixed Effects
|
Sum Sq
|
Mean Sq
|
NumDF
|
DenDF
|
F value
|
Pr(>F)
|
|
nsamp
|
−0.0003
|
5.3338
|
5.3338
|
1
|
15.3430K
|
1716.7973944
|
0.0
|
|
sampstrat
|
0.0999
|
0.2357
|
0.0786
|
3
|
15.3430K
|
25.2933088
|
2.6 ×
10−16
|
|
K
|
−0.0034
|
0.0432
|
0.0432
|
1
|
15.3430K
|
13.8946308
|
1.9 ×
10−4
|
|
m
|
0.0062
|
0.1486
|
0.1486
|
1
|
15.3430K
|
47.8286525
|
4.8 ×
10−12
|
|
phi
|
0.0043
|
0.0716
|
0.0716
|
1
|
15.3430K
|
23.0482092
|
1.6 ×
10−6
|
|
H
|
0.0009
|
0.0030
|
0.0030
|
1
|
15.3430K
|
0.9623282
|
0.33
|
|
r
|
−0.0040
|
0.0619
|
0.0619
|
1
|
15.3430K
|
19.9363228
|
8.1 ×
10−6
|
[[2]]
|
Contrast
|
Estimate
|
SE
|
Z ratio
|
p
|
|
EG - G
|
−0.0026
|
0.0013
|
−2.0550
|
0.1681013
|
|
EG - R
|
−0.0046
|
0.0013
|
−3.6538
|
1.5 ×
10−3
|
|
EG - T
|
−0.0106
|
0.0013
|
−8.3556
|
4.3 ×
10−14
|
|
G - R
|
−0.0020
|
0.0013
|
−1.5990
|
0.3791273
|
|
G - T
|
−0.0080
|
0.0013
|
−6.3009
|
1.8 ×
10−9
|
|
R - T
|
−0.0060
|
0.0013
|
−4.7013
|
1.5 ×
10−5
|
2.1.3 Megaplots
MEGAPLOT(gdm_ind, "ratio_ae", colpal = "viridis", direction = -1)

MEGAPLOT(gdm_ind, "ratio_err", divergent = TRUE)

MEGAPLOT(gdm_ind, "comboenv_err", divergent = TRUE)

MEGAPLOT(gdm_ind, "geo_err", divergent = TRUE)

2.1.4 Prop NA
Confirm that the distribution of NAs is as expected and the
proportions are small
MEGAPLOT(gdm_ind, "geo_coeff", aggfunc = "prop_na", colpal = "mako")

2.2 Site sampling
2.2.1 Summary plots
gdm_site <- format_gdm(here(p3path, "gdm_sitesampling_results.csv"))
# overall error
summary_hplot(gdm_site, "ratio_ae", colpal = "viridis", direction = -1)

summary_hplot(gdm_site, "env_ae", colpal = "viridis", direction = -1)

summary_hplot(gdm_site, "geo_ae", colpal = "viridis", direction = -1)

# bias
summary_hplot(gdm_site, "ratio_err", divergent = TRUE)

summary_hplot(gdm_site, "comboenv_err", divergent = TRUE)

summary_hplot(gdm_site, "geo_err", divergent = TRUE)

2.2.2 Model summaries
run_lmer(gdm_site, "ratio_ae", filepath = here(p4path, "GDM_site_ratioerr.csv"))
[[1]]
|
Predictors
|
Fixed Effects
|
Sum Sq
|
Mean Sq
|
NumDF
|
DenDF
|
F value
|
Pr(>F)
|
|
nsamp
|
−0.0064
|
14.4681
|
14.4681
|
1
|
8.3500K
|
5.407432e+02
|
5.9 ×
10−116
|
|
sampstrat
|
0.3179
|
8.3211
|
4.1605
|
2
|
8.3500K
|
1.554993e+02
|
5.0 ×
10−67
|
|
K
|
0.0000
|
0.0000
|
0.0000
|
1
|
8.3500K
|
2.777680e-05
|
1.00
|
|
m
|
0.0231
|
1.1117
|
1.1117
|
1
|
8.3500K
|
4.155104e+01
|
1.2 ×
10−10
|
|
phi
|
−0.0049
|
0.0503
|
0.0503
|
1
|
8.3500K
|
1.879243e+00
|
0.17
|
|
H
|
0.0007
|
0.0009
|
0.0009
|
1
|
8.3500K
|
3.448557e-02
|
0.85
|
|
r
|
−0.0137
|
0.3905
|
0.3905
|
1
|
8.3500K
|
1.459672e+01
|
1.3 ×
10−4
|
[[2]]
|
Contrast
|
Estimate
|
SE
|
Z ratio
|
p
|
|
EG - EQ
|
−0.0760
|
0.0044
|
−17.3557
|
0.0
|
|
EG - R
|
−0.0272
|
0.0044
|
−6.1624
|
2.1 ×
10−9
|
|
EQ - R
|
0.0488
|
0.0044
|
11.1986
|
7.3 ×
10−15
|
run_lmer(gdm_site, "geo_ae", filepath = here(p4path, "GDM_site_geoerr.csv"))
[[1]]
|
Predictors
|
Fixed Effects
|
Sum Sq
|
Mean Sq
|
NumDF
|
DenDF
|
F value
|
Pr(>F)
|
|
nsamp
|
−0.0102
|
37.3428
|
37.3428
|
1
|
8.3501K
|
147.9671384
|
9.3 ×
10−34
|
|
sampstrat
|
1.1474
|
145.0126
|
72.5063
|
2
|
8.3501K
|
287.2989940
|
2.2 ×
10−121
|
|
K
|
0.1451
|
43.9976
|
43.9976
|
1
|
8.3500K
|
174.3360187
|
2.1 ×
10−39
|
|
m
|
0.6239
|
813.6420
|
813.6420
|
1
|
8.3500K
|
3223.9742765
|
0.0
|
|
phi
|
0.0080
|
0.1332
|
0.1332
|
1
|
8.3500K
|
0.5279855
|
0.470
|
|
H
|
0.0041
|
0.0344
|
0.0344
|
1
|
8.3500K
|
0.1361223
|
0.710
|
|
r
|
0.0201
|
0.8420
|
0.8420
|
1
|
8.3501K
|
3.3362023
|
0.068
|
[[2]]
|
Contrast
|
Estimate
|
SE
|
Z ratio
|
p
|
|
EG - EQ
|
0.2882
|
0.0134
|
21.4290
|
0.0
|
|
EG - R
|
0.0229
|
0.0136
|
1.6897
|
0.2091145
|
|
EQ - R
|
−0.2652
|
0.0134
|
−19.8333
|
0.0
|
run_lmer(gdm_site, "env_ae", filepath = here(p4path, "GDM_site_enverr.csv"))
[[1]]
|
Predictors
|
Fixed Effects
|
Sum Sq
|
Mean Sq
|
NumDF
|
DenDF
|
F value
|
Pr(>F)
|
|
nsamp
|
−0.0054
|
10.2393
|
10.2393
|
1
|
8.3500K
|
625.37735767
|
3.7 ×
10−133
|
|
sampstrat
|
0.2722
|
1.5358
|
0.7679
|
2
|
8.3500K
|
46.90111271
|
5.6 ×
10−21
|
|
K
|
0.0003
|
0.0002
|
0.0002
|
1
|
8.3500K
|
0.01089743
|
0.920
|
|
m
|
0.0236
|
1.1665
|
1.1665
|
1
|
8.3500K
|
71.24432149
|
3.7 ×
10−17
|
|
phi
|
0.0010
|
0.0022
|
0.0022
|
1
|
8.3500K
|
0.13328216
|
0.720
|
|
H
|
0.0051
|
0.0544
|
0.0544
|
1
|
8.3500K
|
3.32312411
|
0.068
|
|
r
|
−0.0149
|
0.4610
|
0.4610
|
1
|
8.3500K
|
28.15629246
|
1.1 ×
10−7
|
[[2]]
|
Contrast
|
Estimate
|
SE
|
Z ratio
|
p
|
|
EG - EQ
|
−0.0307
|
0.0034
|
−8.9621
|
2.1 ×
10−14
|
|
EG - R
|
−0.0267
|
0.0035
|
−7.7294
|
5.0 ×
10−14
|
|
EQ - R
|
0.0040
|
0.0034
|
1.1684
|
0.4721507
|
2.2.3 Megaplots
MEGAPLOT(gdm_site, "ratio_ae", colpal = "viridis", direction = -1)

MEGAPLOT(gdm_site, "ratio_ae", colpal = "viridis", direction = -1)

MEGAPLOT(gdm_site, "ratio_ae", colpal = "viridis", direction = -1)

MEGAPLOT(gdm_site, "ratio_err", divergent = TRUE)

MEGAPLOT(gdm_site, "comboenv_err", divergent = TRUE)

MEGAPLOT(gdm_site, "geo_err", divergent = TRUE)

2.2.4 Prop NA
Confirm that the distribution of NAs is as expected and the
proportions are small
MEGAPLOT(gdm_site, "geo_coeff", aggfunc = "prop_na", colpal = "mako")

3. Comparison of error distirbutions for MMRR and GDM
mmrr_ind %>%
filter(sampstrat != "full") %>%
ggplot(aes(x = ratio_ae, fill = m, colour = m)) +
geom_density(alpha = 0.5) +
theme_few() +
scale_fill_viridis_d(direction = -1, end = 0.7, begin = 0.3, option = "mako") +
scale_color_viridis_d(direction = -1, end = 0.7, begin = 0.3, option = "mako")

gdm_ind %>%
filter(sampstrat != "full") %>%
ggplot(aes(x = ratio_ae, fill = m, colour = m)) +
geom_density(alpha = 0.5) +
theme_few() +
scale_fill_viridis_d(direction = -1, end = 0.7, begin = 0.3, option = "mako") +
scale_color_viridis_d(direction = -1, end = 0.7, begin = 0.3, option = "mako")
## Warning: Removed 5 rows containing non-finite values (stat_density).

4. Compare results of MMRR and GDM
plot(mmrr_ind$geo_coeff, gdm_ind$geo_coeff, col = gdm_ind$m)
legend("topleft", c("0.25", "1"), col = c("black", "red"), pch = 1)

plot(mmrr_ind$comboenv_coeff, gdm_ind$comboenv_coeff)

df <- data.frame(mmrr_ind,
geo_mg = gdm_ind$geo_coeff/mmrr_ind$geo_coeff,
env_mg = gdm_ind$comboenv_coeff/mmrr_ind$comboenv_coeff,
ratio_mg = gdm_ind$ratio/mmrr_ind$ratio )
summary_hplot(df, "geo_mg")

summary_hplot(df, "env_mg")

summary_hplot(df, "ratio_mg")

MEGAPLOT(df, "geo_mg")

MEGAPLOT(df, "env_mg")

MEGAPLOT(df, "ratio_mg")

plot(mmrr_site$geo_coeff, gdm_site$geo_coeff, col = gdm_site$m)
legend("topleft", c("0.25", "1"), col = c("black", "red"), pch = 1)

plot(mmrr_site$comboenv_coeff, gdm_site$comboenv_coeff)

df <- data.frame(mmrr_site,
geo_mg = gdm_site$geo_coeff/mmrr_site$geo_coeff,
env_mg = gdm_site$comboenv_coeff/mmrr_site$comboenv_coeff,
ratio_mg = gdm_site$ratio/mmrr_site$ratio )
summary_hplot(df, "geo_mg")

summary_hplot(df, "env_mg")

summary_hplot(df, "ratio_mg")

summary_hplot(mmrr_ind, "comboenv_coeff")

summary_hplot(gdm_ind, "comboenv_coeff")

summary_hplot(mmrr_ind, "geo_coeff")

summary_hplot(gdm_ind, "geo_coeff")
